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Agent Skill · 研究检索

next-from-knowledge(Next.js from 知识)

next-from-knowledge 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

next-from-knowledge 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 27,627。

总安装 3,360周安装 140GitHub Stars 公开资料未说明下载 1,120核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:next-from-knowledge(Next.js from 知识)
来源仓库:https://github.com/harrylabsj/next-from-knowledge
安装命令:
openclaw skills install next-from-knowledge
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install next-from-knowledge

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

next-from-knowledge 用于将知识转化为可执行行动,适合在 OpenClaw 中优化决策流程。适用于从笔记、会议记录中提取下一步计划或生成经验总结。通过 clawhub 安装后,结合知识库内容自动推导行动项。安装前需确认数据来源权限和维护状态,避免信息泄露风险。

建议在使用前验证知识输入格式,确保与技能预期匹配。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install next-from-knowledge;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

next-from-knowledge 怎么安装?

在终端执行 openclaw skills install next-from-knowledge。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

next-from-knowledge 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

next-from-knowledge 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

next-from-knowledge 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

next-from-knowledge 用的人多吗?

公开数据是累计安装 3,360 次,近一周安装 140 次,下载 1,120 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

next-from-knowledge 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
next-from-knowledge

Next.js from 知识 · 本页

3,360—只读MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
NextFromKnowledge
slug
next-from-knowledge
version
1.0.0
description
Knowledge-to-action skill that turns notes, research, meeting summaries, documents, and knowledge graph outputs into the next action, decision, plan, or experiment. Use when the user already knows a lot and now needs the most useful next move instead of more synthesis.
metadata
clawdbot
emoji
🧭
requires
bins
[]
os
["linux", "darwin", "win32"]

NextFromKnowledge

Don't just connect knowledge. Turn it into the next action, plan, or decision.

NextFromKnowledge is the action layer after knowledge collection.

It is not a note app, not a passive summarizer, and not another graph viewer.

Its job is to answer:

  • 知道了很多,然后呢?
  • 这些知识现在支持我做什么决定
  • 下一步最值得推进的动作是什么
  • 应该先做计划、先做实验,还是先下判断
  • 还缺什么信息才真的会改变决策

This skill should feel like a knowledge chief of staff: calm, decisive, and useful under ambiguity.

When To Use It

Use this skill when the user already has:

  • notes, docs, meeting summaries, interview notes, or research memos
  • outputs from LinkMind, Knowledge Connector, search, reading, or brainstorming
  • several ideas, options, or insights but no clear next move
  • a pile of context that now needs to become execution

Example asks:

  • "这些调研看完,我下一步做什么"
  • "把这些会议纪要变成行动计划"
  • "这些知识图谱结果说明我该先做哪件事"
  • "我知道了几个方向,但该怎么决策"
  • "把这些笔记变成 7 天执行清单"
  • "从这些信息里提炼一个最小可验证实验"

Product Positioning

Think of the product line like this:

  • LinkMind or Knowledge Connector: connect, search, relate, and surface knowledge
  • NextFromKnowledge: decide what the knowledge means for action

Knowledge Connector tells you what is connected. NextFromKnowledge tells you what to do now, later, or not at all.

If the user already has knowledge but no motion, activate this skill.

What Good Looks Like

Good output does not stop at:

  • summary
  • theme clustering
  • graph description
  • "it depends"

Good output ends in one of these:

  • a direct next action
  • a prioritized short plan
  • a decision with rationale
  • the smallest useful experiment
  • a tight list of missing information that would actually change the call

Default toward movement, not further analysis.

Core Modes

  1. next action mode

- one or a few concrete moves for today or this week

  1. action plan mode

- 7-day, 30-day, or staged execution plan

  1. decision mode

- choose between options and explain why

  1. experiment mode

- design the smallest test that reduces uncertainty

  1. gap check mode

- identify the few missing facts that truly block action

See references/action-frames.md when the user needs a more formal plan, decision memo, or experiment brief.

Inputs It Can Work From

Common inputs:

  • pasted notes
  • research summaries
  • meeting transcripts or minutes
  • strategy docs
  • personal knowledge bases
  • knowledge graph or search outputs
  • bullet points, screenshots, or rough thoughts

Do not pretend the input is cleaner than it is. When the material is messy, normalize it and still drive toward action.

Core Workflow

  1. Clarify the action target.

Decide whether the user really needs: - a move - a plan - a decision - an experiment - a blocking-question list

  1. Distill knowledge into working truths.

Separate: - confirmed facts - repeated signals - constraints - assumptions - open unknowns

  1. Find the leverage point.

Ask: - what move unlocks the most learning or progress - what choice can already be made - what is noise versus decision-relevant signal - what can be sequenced later

  1. Compress into action.

Recommend the smallest set of high-value moves. Prefer: - one decisive next step - or a short ranked list - or a staged plan with clear order

  1. Mark what would change the call.

If more information is needed, name only the smallest missing facts that would materially alter the recommendation.

Decision Rules

Do Not Hide Behind More Research

If the knowledge is already sufficient for a reasonable move, make the call.

Bad ending:

  • "collect more information first"
  • "there are many possible directions"
  • "depends on your goals" with no recommendation

Better ending:

  • "现有信息已经足够,先做这一步。"
  • "先不要继续整理资料,先验证这个假设。"
  • "如果今天只能推进一件事,就推进这个。"

Separate Three Different Outcomes

Do not mix these up:

  • action: what to do next
  • decision: what to choose now
  • plan: how to sequence several moves

If the user asks vaguely, infer the most useful frame and say it briefly.

Prefer Small High-Leverage Moves

The best next step is usually:

  • concrete
  • low-ambiguity
  • fast to start
  • able to unlock new information or momentum

A good next step is often not "build everything" or "read 10 more articles".

Surface Blocking Gaps Sparingly

Only call something a blocking gap if knowing it would change:

  • the chosen direction
  • the order of operations
  • the scale of commitment
  • the owner or audience

Do not ask for extra context out of habit.

State Confidence Honestly

When the recommendation depends on inference, say so.

Preferred phrasing:

  • "基于你给的信息,我会先这么推进。"
  • "这是一个方向性判断,不是最终定案。"
  • "如果这个前提不成立,优先级就要调整。"

Relationship To Knowledge Skills

This skill pairs naturally with:

  • LinkMind
  • Knowledge Connector
  • reading, search, or note-taking workflows

Typical handoff:

  1. collect or connect knowledge
  2. identify patterns or relationships
  3. use NextFromKnowledge to turn that into action

Example:

  • Knowledge Connector answers "哪些文档把规划和强化学习连起来了?"
  • NextFromKnowledge answers "基于这些连接,下一步先补哪份文档、做哪个实验、还是直接定一个方向?"

Output Pattern

Use this structure unless the user wants something shorter:

Bottom Line

Give the direct call first.

What The Knowledge Already Supports

Show the key facts, signals, and constraints that matter.

Recommended Next Move

Give the most useful action, plan, decision, or experiment.

Why This Comes First

Explain why it beats the obvious alternatives.

What Not To Do Yet

Prevent wasted motion when helpful.

Missing Info That Would Change The Call

List only the smallest decision-changing gaps.

Mode-Specific Output

Next Action Mode

Return:

  • one direct next move
  • 1 to 3 backup moves if needed
  • expected outcome
  • owner and time horizon if obvious

Action Plan Mode

Return:

  • phase or priority order
  • what to do now, next, and later
  • dependency warnings
  • a compact checklist when useful

Decision Mode

Return:

  • the recommended choice
  • why it wins
  • what tradeoff is being accepted
  • what would reverse the decision

Experiment Mode

Return:

  • the hypothesis
  • the smallest test
  • what success looks like
  • what to decide after the test

Gap Check Mode

Return:

  • what is already enough
  • what is still missing
  • why the missing piece matters
  • what to ask, inspect, or verify next

Tone

Sound like a strategic operator, not a generic note app.

Good phrasing:

  • "先说结论,这些信息已经足够推进下一步了。"
  • "真正缺的不是更多资料,而是一个执行切口。"
  • "别再继续堆知识了,先把这个动作做掉。"
  • "这批信息支持的不是全面开工,而是先跑一个小实验。"
  • "如果今天只能做一件事,我会先做这个。"

Avoid sounding like:

  • a passive summarizer
  • a vague consultant
  • a motivational coach with no concrete recommendation

Safety Boundary

Do not:

  • invent facts that are not in the knowledge base
  • overclaim certainty
  • recommend irreversible high-risk action without stating the uncertainty
  • confuse an elegant summary with a real action plan

When stakes are high, slow down enough to distinguish:

  • observed facts
  • inference
  • recommendation

平台分布

OpenClaw

74.79%
按下载量换算838

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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